Text-to-image generators is widely used recently.
Researchers and artists are increasingly concerned about how to use text-to-image generation models to improve the efficiency of artistic creation.
The general generation models will take the text as the input and output the images which match the given text description.
The first work on text-to-image generators is based on GAN [1]}.
By combining text embedding and latent vectors, The authors expect the GAN can generate the corresponding images following the text's guidance.
This work stimulates more researchers to study the test-to-image generation models, but using GAN always cannot achieve good generation results.
